Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

Tumor volume doubling time (TVDT) has been shown to be a potential surrogate marker of biological tumor activity. However, its availability in clinics is strongly limited due to ethical and practical reasons, as its assessment requires at least two subsequent tumor volume measurements in untreated patients. Here, a translational modeling framework to predict TVDT distributions in untreated cancer patient populations from tumor growth data in patient-derived xenograft (PDX) mice is proposed. Eleven solid cancer types were considered. For each of them, a set of tumor growth studies in PDX mice was selected and analyzed through a mathematical model to characterize the distribution of the exponential tumor growth rate in mice. Then, assuming an exponential growth of the tumor mass in humans, the growth rates were scaled from PDX mice to humans through an allometric scaling approach and used to predict TVDTs in untreated patients. A very good agreement was found between model predicted and clinically observed TVDTs, with 91% of the predicted TVDT medians fell within 1.5-fold of observations. Further, exploiting the intrinsic relationship between tumor growth dynamics and progression free survival (PFS), the exponential growth rates in humans were used to generate the expected PFS curves in absence of anticancer treatment. Predicted curves were extremely close to published PFS data from studies involving patient cohorts treated with supportive care or low effective therapies. The proposed approach shows promise as a potential tool to increase knowledge about TVDT in humans without the need of directly measuring tumor dimensions in untreated patients, and to predict PFS curves in untreated patients, that could fill the absence of placebo-controlled arms against which to compare treaded arms during clinical trials. However, further validation and refinement are needed to fully assess its effectiveness in this regard.

Supplementary Fig. S1 from PHA-739358, a potent inhibitor of Aurora kinases with a selective target inhibition profile relevant to cancer

Tables of the energy reduction due to the tumor growth in 3 cell lines

Supplementary Data from A Phase I Dose-Escalation Study of Danusertib (PHA-739358) Administered as a 24-Hour Infusion with and without Granulocyte Colony-Stimulating Factor in a 14-Day Cycle in Patients with Advanced Solid Tumors

Background: Givinostat (ITF2357), an oral, synthetic histone deacetylase inhibitor, significantly improved all histological muscle biopsy parameters in a Phase II study in boys with Duchenne muscular dystrophy (DMD).Research Design and Methods: A population pharmacokinetic (PK) model, including seven clinical studies, was developed to explore the effect of covariates on givinostat PK. The final model was qualified to simulate pediatric dosing recommendations. A PK/pharmacodynamic (PD) model was developed to simulate the link between givinostat plasma concentration and platelet time course in 10-70-kg children following 6 months of givinostat 20-70 mg twice daily. Results: A two-compartment model, with first-order input with lag and first-order elimination from the central compartment, described givinostat PK, demonstrating increasing apparent clearance with increasing body weight. The PK/PD model well-described platelet count time course. Weight-based dosing (arithmetic mean systemic exposure of 554-641 ng center dot h/mL) produced an average platelet count decrease from baseline of 45% with maximum decrease within 28 days. After 1 week and 6 months, similar to 1% and similar to 14-15% of patients, respectively, had a platelet count <75 x 10(9)/L. Conclusions: Based on these data, givinostat dosing will be body weight adjusted and include monitoring of platelet counts to support efficacy and safety in a Phase III DMD study.

Growth chart and host-related model parameters

Figure of simulated plasma concentration profiles in the experiments from a to g

Figures of typical and individual model fittings, VPC, goodness-of-fit for all the experiments (a-g); simultaneous fitting of experiments b,d,e; table of model parameter estimates; plot of hypoxia-mediated resistance model predictions and model parameter estimates

Mathematical details about model formulation and list of model parameters and variables

Cancer anorexia-cachexia syndrome (CACS) is a very severe complication of cancer for which an adequate therapeutic strategy has not yet been defined. Recently, a notable number of new animal models of human CACS has been made available for translational purposes. Under the assumption that tumor-induced adaptations of host metabolism and tumor-host energetic competition play a major role in CACS (together with possible toxicities induced by the anticancer treatment), we developed a new Dynamic Energy Budget (DEB)-based framework, modeling tumor-in-host growth dynamics and cachexia onset in preclinical animal models during anticancer treatments. The tumor-in-host modeling approach was successfully applied on a multitude of in vivo preclinical studies involving different host species, tumor cell lines, type of anticancer agents and experimental settings among which standard xenograft studies. Obtained results strongly suggested the adoption of the tumor-in-host DEB-based approach in the preclinical oncological setting for a joint assessment of drug efficacy and toxicity and for a better design of the experiments. Further applications of the DEB-based approach to the context of poly-targeted combination therapy, anti-cachectic treatments and preclinical to clinical translation are under investigation with extremely encouraging preliminary results.

Health authorities carefully evaluate any change in the batch manufacturing process of a drug before and after regulatory approval. In the absence of an adequate in vitro–in vivo correlation (Level A IVIVC), an in vivo bioequivalence (BE) study is frequently required, increasing the cost and time of drug development. This study focused on developing a Level A IVIVC for progesterone vaginal rings (PVRs), a dosage form designed for the continuous delivery in vivo. The pharmacokinetics (PK) of four batches of rings charged with 125, 375, 750 and 1500 mg of progesterone and characterized by different in vitro release rates were evaluated in two clinical studies. In vivo serum concentrations and in vitro release profiles were used to develop a population IVIVC progesterone ring (P-ring) model through a direct differential-equation-based method and a nonlinear-mixed-effect approach. The in vivo release, Rvivo(t), was predicted from the in vitro profile through a nonlinear relationship. Rvivo(t) was used as the input of a compartmental PK model describing the in vivo serum concentration dynamics of progesterone. The proposed IVIVC P-ring model was able to correctly predict the in vivo concentration–time profiles of progesterone starting from the in vitro PVR release profiles. Its internal and external predictability was carefully evaluated considering the FDA acceptance criteria for IVIVC assessment of extended-release oral drugs. Obtained results justified the use of the in vitro release testing in lieu of clinical studies for the BE assessment of any new PVRs batches. Finally, the possible use of the developed population IVIVC model as a simulator of virtual BE trials was explored through a case study.

Abstract Adequate energy intake and homeostasis are fundamental for the appropriate growth and maintenance of an organism; the presence of a tumor can break this equilibrium. Tumor energy requests can lead to extreme weight loss in animals and cachexia in cancer patients. Angiogenesis inhibitors, acting on tumor vascularization, counteract this tumor–host energy imbalance, with significant results in preclinical models and more limited results in the clinic. Current pharmacokinetic–pharmacodynamic models mainly focus on the antiangiogenic effects on tumor growth but do not provide information about host conditions. A model that can predict energetic conditions that provide significant tumor growth inhibition with acceptable host body weight reduction is therefore needed. We developed a new tumor-in-host dynamic energy budget (DEB)–based model to account for the cytostatic activity of antiangiogenic treatments. Drug effect was implemented as an inhibition of the energy fraction subtracted from the host by the tumor. The model was tested on seven xenograft experiments involving bevacizumab and three different tumor cell lines. The model successfully predicted tumor and host body growth data, providing a quantitative measurement of drug potency and tumor-related cachexia. The inclusion of a hypoxia-triggered resistance mechanism enabled investigation of the decreased efficacy frequently observed with prolonged bevacizumab treatments. In conclusion, the tumor-in-host DEB-based approach has been extended to account for the effect of bevacizumab. The resistance model predicts the response to different administration protocols and, for the first time, the impact of tumor-related cachexia in different cell lines. Finally, the physiologic base of the model strongly suggests its use in translational human research. Significance: A mathematical model describes tumor growth in animal models, taking into consideration the energy balance involving both the growth of tumor and the physiologic functions of the host.

Background and objectiveIn Duchenne Muscular Dystrophy (DMD) treatment, muscle fiber size can be considered as an indicator of muscle health and function. In particular, the statistical distribution of fibers cross-sectional areas (CSAs) has been used as quantitative efficacy endpoint. For each patient, assessment of treatment effect relies on the comparison of pre- and post-treatment biopsies. Since biopsies provide “distributional data”, i.e. empirical distributions of fibers CSA, the comparison must be carried out between the empirical pre- and post-treatment distributions.MethodsHere, distributional fiber CSA data are analyzed by means of a hierarchical statistical model based on the population approach, considering both the single patient and the population level.ResultsThe proposed method was used to assess the histological clinical effects of Givinostat, a compound under study for DMD treatment. At the single patient level, a two-component Gaussian mixture adequately represents pre- and post-treatment distributions of log-transformed CSAs; drug effect is described via a dose-dependent multiplicative increase of muscle fiber size. The single patient model was also validated via muscle composition data. At the patient population level, typical model parameters and inter-patient variabilities were obtained.ConclusionsThe proposed methodological approach completely characterizes fiber CSA distributions and quantifies drug effect on muscle fiber size, both at the single patient and at the patient population level. This approach might be applied also in other contexts, where outcomes measured in terms of distributional data are to be assessed.

Host features, such as cell proliferation rates, caloric intake, metabolism and energetic conditions, significantly influence tumor growth; at the same time, tumor growth may have a dramatic impact on the host conditions. For example, in clinics, at certain stages of the tumor growth, cachexia (body weight reduction) may become so relevant to be considered as responsible for around 20% of cancer deaths. Unfortunately, anticancer therapies may also contribute to the development of cachexia due to reduced food intake (anorexia), commonly observed during the treatment periods. For this reason, cachexia is considered one of the major toxicity findings to be evaluated also in preclinical studies. However, although various pharmacokinetic-pharmacodynamic (PK-PD) tumor growth inhibition (TGI) models are currently available, the mathematical modeling of cachexia onset and TGI after an anticancer administration in preclinical experiments is still an open issue. To cope with this, a new PK-PD model, based on a set of tumor-host interaction rules taken from Dynamic Energy Budget (DEB) theory and a set of drug tumor inhibition equations taken from the well-known Simeoni TGI model, was developed. The model is able to describe the body weight reduction, splitting the cachexia directly induced by tumor and that caused by the drug treatment under study. It was tested in typical preclinical studies, essentially designed for efficacy evaluation and routinely performed as a part of the industrial drug development plans. For the first time, both the dynamics of tumor and host growth could be predicted in xenograft mice untreated or treated with different anticancer agents and following different schedules. The model code is freely available for downloading at http://repository.ddmore.eu (model number DDMODEL00000274).

Duchenne Muscular Dystrophy (DMD) is caused by mutations in the dystrophin gene leading to dystrophin deficiency, muscle fiber degeneration and progressive fibrotic replacement of muscles. Givinostat, a histone deacetylase (HDAC) inhibitor, significantly reduced fibrosis and promoted compensatory muscle regeneration in mdx mice. This study was conducted to evaluate whether the beneficial histological effects of Givinostat could be extended to DMD boys. Twenty ambulant DMD boys aged 7 to <11 years on stable corticosteroid treatment were enrolled in the study and treated for ≥12 months with Givinostat. A muscle biopsy was collected at the beginning and at the end of treatment to evaluate the amount of muscle and fibrotic tissue. Histological effects were the primary objectives of the study. Treatment with Givinostat significantly increased the fraction of muscle tissue in the biopsies and reduced the amount of fibrotic tissue. It also substantially reduced tissue necrosis and fatty replacement. Overall the drug was safe and tolerated. Improvement in functional tests was not observed in this study, but the sample size of the study was not sufficient to draw definitive conclusions. This study showed that treatment with Givinostat for more than 1 year significantly counteracted histological disease progression in ambulant DMD boys aged 7 to 10 years.

The development of a drug is a long and increasingly expensive process. Mathematical models have become fundamental tools for the most efficient use of resources in pharmaceutical research, driving the design of more informative studies and therefore reducing the number of animals or patients evaluated. Models provided with some mechanistic ground allow discriminating drug-related parameters from system-related parameters, which is a fundamental requirement for predicting the effect of a new treatment in different studies and settings. A tumor growth inhibition model of this kind, which describes the effect of oncology agents in xenograft mice, has been developed. The model returns a measure of drug potency and allows the calculation of the minimal concentration needed to stop the tumor growth. The model has been extended to different scenarios, like combination therapy. Even more importantly, it has been possible to determine a relationship between the drug potency parameters estimated in animals and efficacious clinical exposures in oncology patients, further supporting the robustness of the model in guiding the decision making during the drug development process.